A unified framework for concurrent pedestrian and cyclist detection

Xiaofei Li, Lingxi Li, F.B. Flohr, Jianqiang Wang, Hui Xiong, Morys Bernhard, Shuyue Pan, D. M. Gavrila, Keqiang Li

Research output: Contribution to journalArticleScientificpeer-review

85 Citations (Scopus)


Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.
Original languageEnglish
Pages (from-to)269 - 281
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number2
Publication statusPublished - 2017


  • upper body detection
  • Multiple potential regions
  • pedestrian and cyclist detection
  • R-CNN


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